The online advertising industry uses data (in particular behavioral targeting data) to fuel advertising campaigns by optimizing ad placement, ad content, real-time bidding etc. This data is also referred to as “audience data”, since it is associated with a set of users—it includes demographic data (e.g. gender, age), psychographic data (e.g. interests, opinions), geographic data (e.g. zip code, state), as well as in-market data (e.g. users being in market for luxury cars, travel to Polynesia, etc). Audience data is aggregated from multiple sources over time from users' multiple online actions and is typically organized around individual users into online user profiles. These user profiles are usually de-identified—i.e. no personal identifiable information such as name, email address, SSN is included.
The online advertising industry is results driven: ad campaign metrics are implemented and monitored constantly—behaviorally targeted advertising being the most sensitive to performance; advertisers most frequently adjust and tune the answer to the question “what user audience should a campaign target to optimize a set of key performance indicators?” Due to expected high performance, behaviorally targeted ad campaigns are bought at a premium price. This leads advertisers to plan and model the results of these campaigns in advance, even before the campaigns are commissioned.
Additionally, when advertisers run media campaigns, they track metrics such as conversion rate and effective CPM (cost per thousand impressions) rates, to determine campaign effectiveness. During a campaign, as well as after a campaign has run, the advertisers want to assess pertinent information, including: (1) how effective would a campaign be if a certain type of behavior targeting data was used, such as West Coast green shoppers, who have two or more children; (2) how effective would a campaign be if the behavior targeting data was provided by one source vs. multiple sources; and (3) analytics—what are useful characteristics of converters (users who end up reaching an end-goal, e.g. purchasing a product, visiting a website etc): e.g. converters are twice as likely to be in market for a trip to Southeast Asia, but less likely to buy a luxury automobile compared to the average web user. Answering these questions provides useful analytics in the form of actionable insights, meaningful metrics, and better campaign planning and performance optimization.
Online advertising systems have at their disposal considerable information to assist in the process of campaign planning, yet significant hurdles exist to make behavior targeting usable—for example if behavioral data is available in aggregate but cannot be linked to individual impressions or users, its usefulness is limited and its impact on ROI reduced. For example, campaign planners typically have access to (i) impression log data, which contain the individual impressions served: a unique user identifier, the impression timestamp, creative id, placement id, creative type, creative size etc. Campaign planners also leverage (ii) behavioral targeting data, which is collected and managed separately by a different set of vendors; this data also includes a unique user identifier, user's actions on multiple websites and their timestamps etc. Since the two types of data (i) and (ii) are typically collected and managed disjointly, the unique identifiers assigned to users are different and are usually not reconciled. This implies that while planners can understand in aggregate what data works best at a campaign level, they cannot drill down and segment their audiences by performance—i.e. combining impression (media) data with audience data at the individual user level.
To reconcile the two types of data, a process should allow the two sets of user id's to be comparable—e.g., provide a mapping function that correctly assigns a user id from each space to a single user. This mapping can connect a user's online actions to a set of impressions the same user is subsequently exposed to, and possibly to a set of conversions.
Currently, it is difficult to allow user identifiers to be shared, reconciled, or mapped into a common id space. Without such a process, it is difficult to: (1) assess the performance of behavior targeting data in advertising campaigns; (2) provide analytic insights regarding the types of users (profiles) who are shown impressions, click, and convert versus the rest of the user pool; and (3) optimize advertising campaigns to focus on data that leads to the best results (e.g. leading to higher conversion rates).
Typically the flow of data from collection, aggregation, decision, real time bidding, and impression occurs uni-directionally. For example, a user ID at the time of collection is matched against a user in an advertising network for the purpose of deciding how much to bid for an impression. However, the initial user ID is often not propagated further and the connection is lost, i.e., the advertising network does not know the user ID assigned by the behavioral targeting data vendor, only the user ID that it has assigned to the user.
Also, when impressions are reported back to the advertiser, the lack of an explicit unique identifier (user ID) makes it difficult for the advertiser to determine the effectiveness of the behavior targeting data. This lack of direct feedback can lead to poor planning, modeling and optimization. Additionally, the advertiser lacks advanced analytics comparing the performance of media and impressions across different audiences.